Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?. (arXiv:2105.03571v2 [cs.CL] UPDATED)
Dialogue state tracking (DST) plays a key role in task-oriented dialogue
systems to monitor the user's goal. In general, there are two strategies to
track a dialogue state: predicting it from scratch and updating it from
previous state. The scratch-based strategy obtains each slot value by inquiring
all the dialogue history, and the previous-based strategy relies on the current
turn dialogue to update the previous dialogue state. However, it is hard for
the scratch-based strategy to correctly track short-dependency dialogue state
because of noise; meanwhile, the previous-based strategy is not very useful for
long-dependency dialogue state tracking. Obviously, it plays different roles
for the context information of different granularity to track different kinds
of dialogue states. Thus, in this paper, we will study and discuss how the
context information of different granularity affects dialogue state tracking.
First, we explore how greatly different granularities affect dialogue state
tracking. Then, we further discuss how to combine multiple granularities for
dialogue state tracking. Finally, we apply the findings about context
granularity to few-shot learning scenario. Besides, we have publicly released
all codes.